Splunk SPLK-2003 Exam Dumps & Practice Test Questions

Question 1:

Why is the format block commonly used in logic-driven automation or scripting systems?

A. To produce string-based parameters for use in automated functions
B. To build text strings that combine static content with dynamic data for input or display
C. To create lists or arrays for processing in other system blocks
D. To produce styled HTML or CSS for rendering in emails or UI prompts

Correct Answer: B

Explanation:

In logic-based automation tools and low-code platforms, the format block plays a crucial role in constructing meaningful text by merging fixed content with dynamic values. The purpose of this block is not just to concatenate strings, but to intelligently inject variables into predefined sentence structures, enabling dynamic communication and data presentation.

Option B is the correct answer because it accurately describes the format block’s main function: combining static text with dynamic data to form cohesive, human-readable strings. For example, a format block might be used to output "User Jane has logged in from IP 192.168.1.1," where “Jane” and the IP address are variables supplied from user input or system processes. The format block uses placeholders—often labeled as {0}, {1}, and so on—to position the dynamic values within a string template.

Let’s assess the other choices:

• Option A implies that the format block is specifically used to generate parameters for action blocks. While it may indirectly serve this role in practice, that is a byproduct of its core function—not its primary design. Parameters might be built using a formatted string, but the format block’s real strength is in dynamic string composition.

• Option C is incorrect because format blocks handle string operations, not array creation. Arrays are structured collections of elements and are typically handled by other dedicated functions or data manipulation blocks in automation systems.

• Option D mentions generating HTML or CSS content. While technically a format block can construct HTML or CSS as part of a string, its primary purpose is not web styling. Specialized email or UI components are typically responsible for managing content layout and presentation, not the format block itself.

In summary, format blocks are foundational tools in automation systems used to generate customized output by mixing fixed and variable data. Whether it's displaying personalized user messages, logging events, or preparing structured inputs for downstream use, the format block ensures that variable data can be presented in context. This makes B the most accurate and comprehensive choice.

Question 2:

During a second run of a playbook test, a user sees the error: “an empty parameters list was passed to phantom.act().” What does this error most likely indicate?

A. The container contains artifacts, but not usable parameters
B. The playbook is attached to an invalid container
C. The debugger is running in “all” scope mode
D. The debugger is set to “new” scope mode and no new data is present

Correct Answer: D

Explanation:

The error message "an empty parameters list was passed to phantom.act()" signals that an action block in a playbook is being invoked without the required input parameters. This issue is common when testing playbooks in platforms like Splunk SOAR (Phantom), particularly when debugging using specific scopes.

Playbooks in these platforms rely on artifacts—structured data attached to containers—that are used to generate parameters for actions. During testing, the debugger scope determines which artifacts are used to provide these parameters. There are generally two debugger scopes:

• "All": Runs the playbook using all artifacts currently in the container.

• "New": Executes the playbook using only new artifacts added since the last test or run.

Option D is correct. When the debugger is set to “new,” and the second test is initiated without adding new artifacts, the playbook finds no data to act upon. As a result, functions like phantom.act() are called with an empty parameters list, leading to the error. The system is working correctly in this case—it simply has no new inputs to process.

Let’s evaluate the other options:

• Option A is inaccurate. Artifacts are not parameters themselves—they contain the data from which parameters are derived. Having artifacts without generating valid parameters suggests a scope or data-handling issue, not an absence of parameters per se.

• Option B is misleading. An incorrect container might cause other issues (such as no artifacts being present), but the error message specifically points to a lack of parameters, not an invalid container reference.

• Option C is incorrect because if the debugger were set to “all,” this error would not likely occur. The playbook would attempt to process all existing artifacts, increasing the chances of having valid parameters available for the action function.

To prevent this error, users can either switch the debugger scope back to “all” or ensure that new artifacts are added before running a test. Understanding how scope settings affect data visibility is crucial for effective playbook debugging. Thus, the best explanation aligns with Option D.

Question 3:

Within the Investigation page of a security incident platform, which item can be modified or deleted by users?

A. Action results
B. Comments
C. Artifact values
D. Approval records

Correct Answer: B

Explanation:

In platforms like Cortex XSOAR or other SOAR (Security Orchestration, Automation, and Response) solutions, the Investigation page serves as the central workspace where analysts collaborate, review incident details, and track all relevant activities. It includes various sections such as the action log, artifacts, playbook progress, approvals, and user-added comments. These elements contribute to transparency, auditability, and accountability during incident management.

The only component that users can edit or remove within the Investigation page is the comments section (Option B). Comments are meant for internal communication, coordination, or clarification among team members. Since they are manually entered and not part of system-enforced records or logs, the platform typically allows users—depending on permissions—to modify or delete them to correct errors, remove outdated information, or clarify earlier inputs. This flexibility supports dynamic, real-time collaboration during investigations.

Now, let’s address why the other options are not editable:

• Option A: Action results are generated from automated or manual actions triggered through playbooks or analyst intervention. These results form an immutable audit trail, providing transparency and forensic value. Allowing edits or deletions here would compromise the trustworthiness and regulatory compliance of the platform.

• Option C: Artifact values are pieces of technical data—such as URLs, IPs, file hashes, or usernames—collected during an incident. While artifacts can be enriched or supplemented, the original values are preserved for evidentiary integrity. This ensures traceability and reliable context for downstream actions and correlations.

• Option D: Approval records are formal decisions documented during the incident lifecycle, like approvals for quarantining assets or escalating to legal/compliance. These are critical for audit and compliance purposes and cannot be altered, as doing so would violate governance standards and risk tampering with incident resolution logs.

In conclusion, of all the listed elements, only comments are designed to be flexible and modifiable. This design choice balances the need for collaboration and communication without compromising the accuracy or integrity of the investigative evidence. Therefore, the correct answer is B.

Question 4:

What is the main purpose of using a customized workbook in a SOAR environment?

A. To automate event handling through Python scripting
B. To enforce SLAs and track completion on ROI dashboards
C. To guide analysts through structured event and case workflows
D. To use only platform-provided default workflows without customization

Correct Answer: C

Explanation:

In a SOAR platform, a customized workbook acts as a structured, repeatable guide that assists analysts in navigating through the various stages of a security incident or case. The workbook functions like a detailed checklist, helping teams ensure every step in their response process is followed correctly and consistently. Custom workbooks are crucial in tailoring workflows to match an organization’s internal processes, compliance standards, and team responsibilities.

Option C is correct because customized workbooks are intended to guide user activity and coordination during the lifecycle of a security event or case. These workbooks are often divided into phases (e.g., Detection, Containment, Eradication, Recovery) and contain tasks that analysts must complete within each phase. Tasks may be manual, partially automated, or linked to playbooks. This structure ensures that teams adhere to best practices and organizational policies, reducing the chance of oversight or human error.

Let’s examine why the other options are incorrect:

• Option A: While playbooks in SOAR platforms do leverage Python or other scripting methods to automate actions, workbooks themselves are not designed for code-based automation. Workbooks are more focused on human processes and are not vehicles for implementing code or logic.

• Option B: Although workbooks help with task tracking and can support SLA adherence indirectly, they are not the primary tools for applying SLAs or displaying ROI metrics. Those functions are typically handled by reporting modules, dashboards, or performance analytics features within the SOAR platform.

• Option D: This is outright incorrect. One of the core benefits of a SOAR platform is the ability to customize workbooks based on different use cases (e.g., phishing, malware, insider threats). Organizations regularly create their own workbooks or modify existing templates to better align with unique team roles, regulatory needs, and workflows.

To summarize, customized workbooks in SOAR enhance operational consistency, support structured incident response, and act as a roadmap for analysts. They are vital tools for maintaining a thorough, repeatable approach to security case management. Hence, Option C is the most accurate choice.

Question 5:

Which of the following represents the correct default ports that should be configured in Splunk to enable successful communication with a SOAR platform?

A. SplunkWeb (8088), SplunkD (8089), HTTP Collector (8000)
B. SplunkWeb (8472), SplunkD (8589), HTTP Collector (8962)
C. SplunkWeb (8000), SplunkD (8089), HTTP Event Collector (8088)
D. SplunkWeb (8089), SplunkD (8088), HTTP Collector (8000)

Answer: C

Explanation:

When integrating a SOAR (Security Orchestration, Automation, and Response) platform—such as Cortex XSOAR—with Splunk, it is essential to configure Splunk with the correct default ports to ensure seamless connectivity and functionality. Splunk operates multiple internal services, each assigned a specific default port to manage tasks such as user access, REST API communication, and data ingestion through HTTP. These ports must be open and correctly mapped in both Splunk and the SOAR platform for proper integration.

The default ports for a standard Splunk deployment are as follows:

• SplunkWeb (Port 8000): This is the default port used for accessing the Splunk web interface. It allows users and administrators to log in, configure dashboards, view reports, and manage settings.

• SplunkD (Port 8089): This port handles internal management communications and REST API calls. For SOAR integrations, this port enables automated systems to execute search queries, retrieve results, and interact with Splunk programmatically.

• HTTP Event Collector (Port 8088): Commonly referred to as HEC, this service listens for incoming data sent over HTTP or HTTPS. SOAR platforms often use this port to push event data or alerts into Splunk in real-time, which makes it critical for integrations involving event forwarding.

Option C correctly identifies these standard default ports and their respective services, making it the right choice.

Let’s examine the incorrect options:

• Option A misaligns the port numbers. It assigns 8088 to SplunkWeb (incorrect—it belongs to HEC) and 8000 to HEC (incorrect—it’s for SplunkWeb), making this setup invalid.

• Option B lists arbitrary and non-standard ports (8472, 8589, 8962), which are not assigned to Splunk services by default. These values would only apply if the Splunk instance had been customized, which isn’t typical or assumed unless specified.

• Option D reverses the service-port mapping, placing SplunkWeb on 8089 (which actually belongs to SplunkD) and assigning 8000 to HEC (incorrect). Again, this setup would not function correctly without extensive manual reconfiguration.

Therefore, for proper communication between SOAR platforms and Splunk using default configurations, Option C is the accurate choice.

Question 6:

In Splunk SOAR, which attribute must containers share for an active playbook to automatically trigger on them?

A. Tag
B. Label
C. Artifact
D. Severity

Answer: B

Explanation:

In Splunk SOAR (Security Orchestration, Automation, and Response), automation is driven through playbooks, which are structured workflows designed to respond to incoming events or containers. For a playbook to run automatically upon container ingestion, there must be a clear association between the playbook and a characteristic of the container. This characteristic is the label.

Labels in SOAR serve as a type of categorization or classification assigned to containers when they are brought into the system. Labels can describe the nature of the event, such as "Phishing", "Endpoint Malware", or "Firewall Alert". When an administrator creates or modifies a playbook, they must explicitly define which label(s) the playbook should react to. If an incoming container has a label that matches a playbook’s configuration and the playbook is set to active, it will automatically execute against that container.

Option B, Label, is therefore the correct answer, as it is the primary trigger condition used in Splunk SOAR for playbook execution. Labels provide a scalable and manageable way to organize and automate incident handling across different use cases.

Let’s analyze the other options:

• Option A: Tag – Tags are used for organizing, searching, and filtering containers within the SOAR UI. They assist analysts with manual classification or investigation but do not influence whether a playbook runs. Tags are metadata, not trigger conditions.

• Option C: Artifact – Artifacts are the data elements within a container, such as IP addresses, hashes, URLs, etc. While playbooks use artifact data during execution, the presence of specific artifacts doesn’t initiate playbook execution by default. Artifacts are inputs, not triggers.

• Option D: Severity – Severity indicates the priority or urgency of a container (e.g., Low, Medium, High, Critical). While severity can be used within a playbook for conditional logic (e.g., taking different actions depending on severity level), it does not determine whether the playbook will run automatically.

In summary, labels are the foundational mechanism that link containers to active playbooks in Splunk SOAR. This allows for clear, rule-based automation without relying on dynamic content or manual input. Hence, the correct answer is B: Label.

Question 7:

Which block in the SOAR visual playbook editor is specifically designed to create dynamic Splunk search queries by combining text and context-based variables?

A. Action block
B. Filter block
C. Prompt block
D. Format block

Correct Answer: D

Explanation:

In SOAR platforms like Cortex XSOAR, playbooks are built using a visual editor that includes a variety of functional blocks. Each block type has a distinct purpose that contributes to the playbook’s overall automation workflow. When integrating with search-based systems such as Splunk, it becomes necessary to dynamically assemble search strings using live data — and this is precisely where the format block becomes essential.

The format block is engineered to create custom, structured strings by merging static text with dynamic data extracted from previous playbook actions or incident fields. This functionality is especially useful when you need to construct Splunk search queries that depend on values like IP addresses, hostnames, usernames, or alert data collected earlier in the playbook.

For example, if an IP address was retrieved from an incident artifact and needs to be searched in Splunk logs, the format block would enable the following dynamic string construction:

Here, $ip_address is replaced at runtime with the actual value retrieved earlier. Once the string is properly constructed, it can be handed off to an action block that executes the search using the Splunk integration.

Now let’s clarify the incorrect options:

• Action block (A): Executes actions via integrations like Splunk, but it requires a fully formed input string (such as a search query) and cannot construct that string on its own.

• Filter block (B): Evaluates logic or conditions and helps determine which path the playbook should follow. It doesn't build strings or construct queries.

• Prompt block (C): Used for manual intervention, where a human operator is prompted to provide input or approve a decision. It plays no role in query construction.

In conclusion, when constructing a Splunk search string in a SOAR playbook, the correct approach is to use the format block to build the string, and then pass it to an action block for execution. This makes D the most accurate and effective choice.

Question 8:

In a SOAR playbook, how should you configure a decision block to evaluate the country code returned by the geolocate_ip_1 action against a list of banned countries?

A. Use file_reputation_2:action_result.data..response_code, operator ==, value custom_list:Banned Countries
B. Use geolocate_ip_1:action_result.data..country_iso_code, operator in, value custom_list:Banned Countries
C. Use geolocate_ip_1:action_result.cef..country_iso_code, operator !=, and leave the value field blank
D. Use file_reputation_2:action_result.cef..response_code, operator in, value United States

Correct Answer: B

Explanation:

In Splunk SOAR (formerly known as Phantom), decision blocks are used to evaluate outputs from previous action blocks and guide the playbook logic based on the results. This is a powerful feature when implementing conditional workflows such as blocking traffic from risky regions or escalating incidents based on file reputation.

In the given scenario, two action blocks — geolocate_ip_1 and file_reputation_2 — feed data into a decision block. The goal is to compare the country returned from an IP geolocation against a custom list of high-risk countries (e.g., “Banned Countries”).

Option B is correct because it accurately:

1. Selects the correct action result field: geolocate_ip_1:action_result.data..country_iso_code retrieves the ISO country code (e.g., "CN", "RU") from the geolocation action.

2. Uses the proper evaluation operator: in is used to check whether the returned value is present in a defined list.

3. Compares against a relevant custom list: custom_list:Banned Countries is a predefined list that stores ISO codes of countries deemed unsafe or prohibited.

This setup ensures the playbook can intelligently branch or trigger alerts based on the originating country of the IP address.

Let’s evaluate the incorrect options:

• Option A evaluates file_reputation_2:action_result.data..response_code against a list of country names. This is a mismatch of data types, since response_code typically returns threat classification values (like benign, malicious), not geographic data.

• Option C uses the cef field, which is typically associated with artifacts, not action results. Additionally, using != without specifying a comparison value creates a logic error and would fail validation.

• Option D compares a threat response code field to the string "United States". This again mismatches the data types — response codes are generally numerical or classification strings, not country names.

In conclusion, Option B correctly applies SOAR logic by comparing the ISO country code from a geolocation action to a custom list. This enables dynamic decision-making based on the IP’s origin, which is a common use case in threat detection and automated response.

Question 9:

A Splunk administrator is setting up index retention policies to manage disk space efficiently. Which two settings are most important when configuring data retention in indexes.conf? 

A. maxDataSize
B. maxTotalDataSizeMB
C. frozenTimePeriodInSecs
D. maxHotSpanSecs
E. homePath.maxDataSizeMB

Correct Answers: B, C

Explanation:

In Splunk, efficient data lifecycle management is critical for ensuring that indexes do not consume excessive disk space and that data retention complies with organizational policies. The indexes.conf file plays a central role in managing how long data is retained and how much space an index can use.

Option B, maxTotalDataSizeMB, sets the maximum size of an index in megabytes. Once this limit is reached, the oldest buckets are rolled to frozen (or deleted, if no archiving script is configured). This setting is essential for controlling the physical space an index consumes and is often used to enforce disk usage quotas.

Option C, frozenTimePeriodInSecs, defines the time-based retention policy for an index. It determines how long data will remain searchable in the index. When data exceeds this age, the corresponding bucket is frozen—typically meaning it's deleted unless a script moves it to a cold storage location. This is the primary time-based retention setting and is crucial for ensuring compliance and keeping storage usage manageable.

Option A, maxDataSize, applies to hot buckets and affects when they roll to warm. While this influences performance and data organization, it is not directly related to long-term retention or space management.

Option D, maxHotSpanSecs, controls how long a hot bucket can remain open before being rolled to warm. It is more about bucket lifecycle timing than storage limitation or data retention.

Option E, homePath.maxDataSizeMB, is not a valid setting in indexes.conf. The correct setting is maxTotalDataSizeMB for total index size management.

In conclusion, to manage data retention effectively in Splunk, administrators rely primarily on frozenTimePeriodInSecs for time-based expiration and maxTotalDataSizeMB to enforce a size limit on each index. These two settings ensure that disk resources are used efficiently and that outdated data is removed in a timely manner.

Question 10:

An administrator needs to manage user authentication and assign roles in Splunk. What are two valid methods for managing user accounts and roles in Splunk Enterprise? 

A. Creating users and assigning roles directly via Splunk’s UI
B. Managing users exclusively using the CLI with custom scripts
C. Integrating with LDAP or Active Directory for authentication and role mapping
D. Editing dashboard XML files to define user roles
E. Modifying outputs.conf to configure role-based access

Correct Answers: A, C

Explanation:

User and role management in Splunk Enterprise is a critical aspect of securing access to data and controlling user capabilities within the platform. Splunk provides multiple ways to manage this, both locally and through external systems.

Option A, creating users and assigning roles via the UI, is the most straightforward and common method. Splunk’s web interface includes a dedicated Access Controls section where administrators can define users, assign them roles, and specify capabilities. This method is especially useful in smaller environments or for quick testing.

Option C, integrating with LDAP/Active Directory, is another highly effective method for larger enterprises. This allows organizations to centrally manage users and groups using their existing identity infrastructure. When integrated, Splunk can map LDAP groups to Splunk roles, automating user provisioning and ensuring alignment with enterprise access policies. It also simplifies credential management since users log in using their corporate credentials.

Option B, managing users exclusively via CLI, is not standard practice. While Splunk’s configuration files can be edited manually, doing so with custom scripts introduces risks and lacks real-time visibility. It is unsupported and not scalable.

Option D, editing dashboard XML to define roles, is incorrect. Dashboards define UI components and visualizations but are not used for access control or role management.

Option E, modifying outputs.conf, relates to data forwarding configuration, not user authentication or authorization.

To summarize, the recommended and supported approaches for user and role management in Splunk Enterprise are local user/role creation via the UI and integration with directory services like LDAP/AD. These methods provide secure, scalable, and auditable access control essential for enterprise environments.